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2021 NBA Draft Safari

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So I'm not fully convinced of this observation yet, gotta watch a lot more OK State games, but what do you guys think of the theory that Cade isn't limited by athleticism so much as he's got a pretty bad, high dribble? I noticed on a lot of the recent games where I've watched him, it seems like he doesn't have much of a problem getting to his spots (in the paint and pulling up/stepping back for jumpers), but at the same time he's good for multiple times a game where he just loses the ball, sometimes just carelessly before he even attacks.

It's kinda both, right? He's not a very good athlete. He's not a very good ballhandler. He's not even a very good passer...some of his entry passes against Baylor were just poor ideas and poorly executed leading to live-ball turnovers. What he's got going for him is he's long, and he can really shoot the ball well. But there are numerous reasons to doubt whether he'll be able to create offense off the dribble in the NBA.
 
I've produced a prospect ranking based on a blend of my own draft ratings, strength of schedule, and ranking in the recent Hoopshype aggregate mock draft:


Barnes, Sengun, and Moody are pretty solidly 1-2-3. Garuba comes out pretty well thanks to a surprisingly solid scout ranking; he's struggled to carve out a significant role on a stacked Real Madrid squad, but has been respectably efficient in limited minutes vs. a very tough schedule. Giddey also sneaks into the top-10; he's been on an improving trend as I noted recently. (EDIT: Giddey fell a few spots after fixing an issue with pace adjustment for international propects, but still comes out pretty well).
 
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So I'm not fully convinced of this observation yet, gotta watch a lot more OK State games, but what do you guys think of the theory that Cade isn't limited by athleticism so much as he's got a pretty bad, high dribble? I noticed on a lot of the recent games where I've watched him, it seems like he doesn't have much of a problem getting to his spots (in the paint and pulling up/stepping back for jumpers), but at the same time he's good for multiple times a game where he just loses the ball, sometimes just carelessly before he even attacks.

I think he has a bit of a high, loose, dribble *and* is a pretty "average" (by NBA standards) athlete. I have absolutely zero to back this up, other than the eye test and gut instincts, but Cade Cunningham reminds me more of Jimmy Butler (offensively), with a teenie bit of Paul George, more than Lebron, Simmons, Luka, Harden, or anyone else. I dont think hes a PG anymore than Butler is, but I can see him being a high usage wing that does some playmaking. Like Butler.
 
I've produced a prospect ranking based on a blend of my own draft ratings, strength of schedule, and ranking in the recent Hoopshype aggregate mock draft:


Barnes, Sengun, and Moody are pretty solidly 1-2-3. Garuba comes out pretty well thanks to a surprisingly solid scout ranking; he's struggled to carve out a significant role on a stacked Real Madrid squad, but has been respectably efficient in limited minutes vs. a very tough schedule. Giddey also sneaks into the top-10; he's been on an improving trend as I noted recently.

@Nathan S - I ask this with absolutely zero criticism (I respect and appreciate your data and opinion). What is the final data point for your sheet? My mother has been a mathematician longer than I've been alive and I work in technology, so numbers and spreadsheets are my language, but I have a hard time figuring out what to focus on on your sheets. It may be *me* being as picky as shifting a column to the far right, or putting the names in rank order. It might also be that I read most of these on my phone.

Again, I hope this isn't coming off as critical, dismissive or unappreciative of your work (I've actually used your numbers to confirm or rethink some of my own opinions).
 
@Nathan S - I ask this with absolutely zero criticism (I respect and appreciate your data and opinion). What is the final data point for your sheet? My mother has been a mathematician longer than I've been alive and I work in technology, so numbers and spreadsheets are my language, but I have a hard time figuring out what to focus on on your sheets. It may be *me* being as picky as shifting a column to the far right, or putting the names in rank order. It might also be that I read most of these on my phone.

Again, I hope this isn't coming off as critical, dismissive or unappreciative of your work (I've actually used your numbers to confirm or rethink some of my own opinions).

Sorry about that! The final column is the blend I mentioned, which takes into account the projected impact from my draft model (the fourth column, "Tot"), strength of schedule (SOS), and rank in the Hoopshype aggregate mock (Scout Rank). And the sheet is sorted according to this blended rating. I've color-coded things to draw attention to strengths and weaknesses within prospect profiles, with blues indicating strengths and reds indicating weaknesses, but maybe this is causing more trouble than it's worth.

EDIT: I really aim to be as transparent as possible with my methodology, so feel free to ask away. I've done lengthier writeups on it in the past, and I think they bore most people, but I could probably find them if you or anyone else is interested in the details.

EDIT2: I changed some of the column headers to be a little bit clearer. I also fixed a small bug affecting pace adjustments for international players, which shifted their model projections slightly.
 
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Sorry about that! The final column is the blend I mentioned, which takes into account the projected impact from my draft model (the fourth column, "Tot"), strength of schedule (SOS), and rank in the Hoopshype aggregate mock (Scout Rank). And the sheet is sorted according to this blended rating. I've color-coded things to draw attention to strengths and weaknesses within prospect profiles, with blues indicating strengths and reds indicating weaknesses, but maybe this is causing more trouble than it's worth.

EDIT: I really aim to be as transparent as possible with my methodology, so feel free to ask away. I've done lengthier writeups on it in the past, and I think they bore most people, but I could probably find them if you or anyone else is interested in the details.

EDIT2: I changed some of the column headers to be a little bit clearer. I also fixed a small bug affecting pace adjustments for international players, which shifted their model projections slightly.

I'm a nerd about this stuff (ask my wife), so I'd definitely be interested in reading any write-up(s) if you have them handy.

The sheet and color-coding is helpful, I guess I was looking for a "final column" that had the results in it. I was trying to fins a column that had Barnes, Sengun and Moody in the top 3 and wasn't having (easy) luck. It looks like your edit took care of that, though.
 
I'm a nerd about this stuff (ask my wife), so I'd definitely be interested in reading any write-up(s) if you have them handy.

The sheet and color-coding is helpful, I guess I was looking for a "final column" that had the results in it. I was trying to fins a column that had Barnes, Sengun and Moody in the top 3 and wasn't having (easy) luck. It looks like your edit took care of that, though.

Sure, here's a post from a couple years ago:

First, a few sentences about how I see the role of statistics in scouting. They're an imperfect source of information among many other imperfect sources of information...they're useful enough to merit consideration, and if you're going to consider them, you might as well do so systematically (i.e. "advanced" statistics, actually finding useful correlations between NCAA stats and NBA performance), not haphazardly (e.g. cherrypicking individual statistics to bolster some preconceived notion about a player). To emphasize how imperfect they are, De'Andre Hunter's per-40 defensive stats stand at 6.3 boards, 0.7 steals, 0.7 blocks. No amount of staring at a box score is going to make you realize that he's a valuable defensive player. There's some hope that more advanced player tracking stats can do a better job, which is probably true, but that's a discussion for another day.

Methodology details below:

---

My model attempts to predict NBA adjusted plus/minus. The major benefit of predicting APM, instead of an NBA box score metric, is that my model doesn't inherit any biases at this stage (e.g. a model that tries to predict PER will necessarily end up with all the same biases/flaws of PER). The major drawback is that adjusted plus/minus is a very noisy stat, and the price I pay for trying to predict a noisy stat is relatively high uncertainties in my model coefficients (e.g. the marginal value of an NCAA assist, or rebound). Ultimately, it's a good tradeoff because in most cases the extra uncertainty in my predictions due to uncertainty in model coefficients is small relative to other sources of uncertainty.

My model assumes that there are no interaction terms between parameters. That means that the value of an NCAA player's assist according to my model does not depend at all on how many rebounds he gets, or how many points he scores.

It turns out that this assumption is absolutely crucial. Without it, a model is extremely vulnerable to a problem called "overfitting" in which it's basically tricked into thinking some artifact of statistical noise that affected a few prospects in the past is a fundamental rule that applies to all prospects in the future. A model suffering from overfitting generally does a good job explaining outcomes for past prospects but produces wonky and inaccurate predictions for future prospects.

The last important thing my model does is estimate uncertainties in its predictions, something notably lacking from most such models people have published. This illuminates some of the strengths and weaknesses of my model. For example, the largest contributor to uncertainty is made two-pointers, that is, my model is generally less accurate in predicting players who make a lot of two pointers. This makes some sense; a player's two pointers made per game (even taken together with two point percentage, or equivalently two pointers missed) falls far short of describing how good a scorer the player really is inside the arc. There's just not enough information in this part of the box score to properly evaluate a player.

Some more minor things:

-All stats are per possession. I also include height, and minutes per game.

-I assume a quadratic aging curve. I found that on offense, better prospects actually follow a steeper aging curve than worse prospects, and I accounted for this as well.

-My sample only goes through the 2012 draft. This hurts my sample size, and also hurts because my model is really tuned to predict how players entering the NBA a decade ago would be expected to perform. Obviously the NBA has changed since then and I have no way of adjusting for that.

-My sample only includes prospects that went on to play significant NBA minutes, so it suffers from "survivor bias" and therefore tends to be slightly too optimistic in its projections. How to correct for this is an interesting question in its own right that I won't get into for now (but could talk more about if you're interested).

-My model has some interesting artifacts because of the relatively large uncertainties in the coefficients I mentioned earlier. For instance, made two pointers have a slight (not statistically significant) negative value. In reality, they probably have (at least) a slight positive value. I could manually correct things like this to make my model slightly better, but that's obviously a slippery slope toward tweaking and tuning my model in retrospect to make it look like I think it "should." So I decided to just let it be, even in cases where the helpful tweak is obvious.

-My model doesn't account for strength of schedule or team strength.
 
After adding another 5 dimes this morning, Sengun's now averaging 2.4 assists per game. As far as I know, the only 18-year-old center who averaged more (in the NCAA or internationally) was Jokic, who averaged 2.5.

 
After adding another 5 dimes this morning, Sengun's now averaging 2.4 assists per game. As far as I know, the only 18-year-old center who averaged more (in the NCAA or internationally) was Jokic, who averaged 2.5.


Give me Sengun!
 
Where is Suggs on everyone's personal boards now? Haven't been keeping tabs on Gonzaga but I saw that he's now shooting 25% from 3 in conference play. The sweet-shooting Suggs is definitely a top 3-5 pick, but does he deserve it with his current shooting over a larger sample size?
 
Where is Suggs on everyone's personal boards now? Haven't been keeping tabs on Gonzaga but I saw that he's now shooting 25% from 3 in conference play. The sweet-shooting Suggs is definitely a top 3-5 pick, but does he deserve it with his current shooting over a larger sample size?

I would put him around 5/6.

Mobley, Barnes, Green, Cade I'm personally taking over him.

To me, at 5/6/7 it is pick 3 of Kuminga, Suggs, Johnson, Moody, Springer.

Suggs has pretty good numbers still but the PG position is just so competitive. And in a draft like this, unless a lead guard just has elite analytical numbers (ala like a Ja Morant), I just can't see the justification for taking him over Mobley / Barnes / Green.....maybe Cade depending on team need.
 
I would put him around 5/6.

Mobley, Barnes, Green, Cade I'm personally taking over him.

To me, at 5/6/7 it is pick 3 of Kuminga, Suggs, Johnson, Moody, Springer.

Suggs has pretty good numbers still but the PG position is just so competitive. And in a draft like this, unless a lead guard just has elite analytical numbers (ala like a Ja Morant), I just can't see the justification for taking him over Mobley / Barnes / Green.....maybe Cade depending on team need.

Have you run the numbers on Sengun yet?
 
Have you run the numbers on Sengun yet?

I either need per 100....or I have a calculation that can use pace + per 36/40, to get an accurate estimate of per 100.

Those are the two calculations I have that can determine a PDIFF number in my spreadsheet.

Do you know of somewhere that tracks that for foreign prospects? Per 100 stats?

I have had a lot of trouble in the past getting those numbers publicly, so if there is an interesting prospect, I will sometimes ping a few of the friends I have in NBA team circles who can easily get it (like Ball last year). But I typically wait until all games are played, so I don't have to ask again. :chuckle:
 

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